Example Concept: AI systems can be prone to biases if the training data is not representative or if the algorithms are not properly calibrated. This can lead to skewed decision-making that does not accurately reflect the business environment. Additionally, AI models may not adapt well to unexpected changes or novel situations, which can result in poor decision outcomes if not monitored by human experts.